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Duration 21 hours
Course Outline
Foundations of Large Language Models
- Foundamentals of Natural Language Processing (NLP)
- Basics of Large Language Models (LLMs)
- The role of Meta AI in advancing LLM technology
Analyzing the Structure of Meta AI LLMs
- Exploration of Transformer architecture and self-attention processes
- Methods for training models at scale
- Contrasting Meta AI models with other LLMs like GPT, BERT, and T5
Establishing the Development Workspace
- Setup and configuration of Python and Jupyter Notebook
- Interaction with Hugging Face and Meta AI’s model libraries
- Leveraging cloud or local GPU resources for computational tasks
Refining and Adapting Meta AI LLMs
- Acquiring pre-trained model weights
- Adjusting models using industry-specific data sets
- Application of transfer learning strategies
Creating NLP Solutions with Meta AI LLMs
- Construction of chatbots and interactive AI agents
- Implementation of text summary tools and paraphrasing engines
- Application of sentiment analysis and content filtering
Enhancing Performance and Deployment
- Optimizing processing speeds for inference
- Techniques for model reduction and quantization
- Distribution of LLMs via API endpoints and cloud infrastructures
Ethical Frameworks and Responsible AI
- Identifying and reducing bias within LLMs
- Promoting transparency and equitable outcomes in AI systems
- Insights into emerging trends and future directions in AI
Requirements
- A foundational grasp of machine learning and deep learning principles
- Proficiency in Python coding
- Knowledge of core Natural Language Processing (NLP) concepts
Target Audience
- Professionals in AI Research
- Data Science Specialists
- Engineers focused on Machine Learning
- Software Developers with an interest in NLP technologies
